Create wit.py

wit.py

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+import json
+
+import datasets
+from datasets import Value, Sequence, Features
+
+
+_CITATION = """\
+@article{srinivasan2021wit,
+  title={WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning},
+  author={Srinivasan, Krishna and Raman, Karthik and Chen, Jiecao and Bendersky, Michael and Najork, Marc},
+  journal={arXiv preprint arXiv:2103.01913},
+  year={2021}
+}
+"""
+
+_DESCRIPTION = """\
+Wikipedia-based Image Text (WIT) Dataset is a large multimodal multilingual dataset. WIT is composed of a curated set
+ of 37.6 million entity rich image-text examples with 11.5 million unique images across 108 Wikipedia languages. Its
+ size enables WIT to be used as a pretraining dataset for multimodal machine learning models.
+"""
+
+_HOMEPAGE = "https://github.com/google-research-datasets/wit"
+
+_URL = "https://storage.googleapis.com/huggingface-nlp/datasets/wit/"
+_URLS = {
+    # TODO - This should be in range(400). Haven't mirrored all the files yet.
+    'train': [_URL + f"part-{'%05d' % i}-48a6f07e-bb86-4735-aac7-883349f41a28-c000.json.gz" for i in range(2)]
+}
+
+class Wit(datasets.GeneratorBasedBuilder):
+    """WIT: Wikipedia-based Image Text Dataset for Multimodal Multilingual Machine Learning"""
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=Features({
+                'b64_bytes': Value('string'),
+                'embedding': Sequence(Value('float64')),
+                'image_url': Value('string'),
+                'metadata_url': Value('string'),
+                'wit_features': Sequence({
+                    "language": Value('string'),
+                    "page_url": Value('string'),
+                    "image_url": Value('string'),
+                    "attribution_passes_lang_id": Value("string"),
+                    "caption_alt_text_description": Value('string'),
+                    "caption_attribution_description": Value('string'),
+                    "caption_reference_description": Value('string'),
+                    "caption_title_and_reference_description": Value('string'),
+                    "context_page_description": Value('string'),
+                    "context_section_description": Value('string'),
+                    "hierarchical_section_title": Value('string'),
+                    "is_main_image": Value('string'),
+                    "mime_type": Value('string'),
+                    "original_height": Value('string'),
+                    "original_width": Value('string'),
+                    "page_changed_recently": Value('string'),
+                    "page_title": Value('string'),
+                    "section_title": Value('string'),
+                })
+            }),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        urls_to_download = _URLS
+        downloaded_files = dl_manager.download_and_extract(urls_to_download)
+        print(downloaded_files)
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files["train"]}),
+        ]
+
+    def _generate_examples(self, filepaths):
+        """Yields examples."""
+        wit_feature_names = self.info.features['wit_features'].feature.keys()
+        for filepath in filepaths:
+            with open(filepath, "rb") as f:
+                for i, line in enumerate(f):
+                    line = line.strip()
+                    row_data = json.loads(line, encoding='utf-8')
+                    for feature in row_data['wit_features']:
+                        for fname in wit_feature_names:
+                            if fname not in feature:
+                                feature[fname] = None
+                    yield str(i), row_data